In recent years, JupyterLab has rapidly become the tool of choice for data scientists, machine learning (ML) practitioners, and analysts worldwide. This powerful, web-based integrated development ...

In today’s fast-changing data landscape, having a strong data system and advanced analytical tools is key to getting valuable insights and staying ahead of the competition. The data lakehouse ...

Recent advancements in machine learning have ushered in a transformative era for seismic data analysis. By integrating sophisticated algorithms such as convolutional neural networks (CNNs), generative ...

Forbes contributors publish independent expert analyses and insights. Writes about the future of payments. We live in a world where machines can understand speech, recognize faces, and even generate ...

The data science and machine learning technology space is undergoing rapid changes, fueled primarily by the wave of generative AI and—just in the last year—agentic AI systems and the large language ...

The rapid acceleration of AI adoption across industries is reshaping not only products, but also the engineering roles that support them. As organizations move machine learning systems from ...

Databricks has added a new agent, the Data Science Agent, to the Databricks Assistant, in an effort to help data practitioners automate analytics tasks. The agent, which is available now in preview ...

R is regaining attention in 2026, especially in statistics-heavy and research-focused data science work.Python still leads in ...

Advocates of FinTech lending argue that it enables lenders to predict loan outcomes more accurately by employing complex analytical tools, such as machine learning (ML) methods. The authors of this ...